US11006842B2 - Non-invasive brachial blood pressure measurement - Google Patents

Non-invasive brachial blood pressure measurement Download PDF

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US11006842B2
US11006842B2 US15/907,693 US201815907693A US11006842B2 US 11006842 B2 US11006842 B2 US 11006842B2 US 201815907693 A US201815907693 A US 201815907693A US 11006842 B2 US11006842 B2 US 11006842B2
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waveform
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cuff
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Ahmad Qasem
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Atcor Medical Pty Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A61B5/02141—Details of apparatus construction, e.g. pump units or housings therefor, cuff pressurising systems, arrangements of fluid conduits or circuits
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A61B5/022—Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
    • A61B5/02225—Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers using the oscillometric method
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6824—Arm or wrist
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
    • A61B2560/0228—Operational features of calibration, e.g. protocols for calibrating sensors using calibration standards

Definitions

  • the invention pertains to measuring systolic and diastolic brachial blood pressure non-invasively using a cuff wrapped around a patient's upper arm.
  • the invention is directed to recalibrating a brachial cuff volumetric displacement waveform so that its maximum and minimum values can be used to accurately estimate the value of the patient's brachial systolic and diastolic blood pressures as measured invasively, such as when using a catheter.
  • Arterial blood pressure is a clinically important indicator of the status of the cardiovascular system, reflective of arterial and cardiac load and an early independent predictive marker of cardiovascular events and diseases.
  • To measure the inter-arterial blood pressure accurately requires an invasive procedure to insert a catheter with a pressure sensor inside the artery.
  • non-invasive methods were created to estimate pressure at the peripheral brachial artery.
  • One of the earliest non-invasive methods to estimate pressure in the brachial artery is the auscultatory method which requires inflating a cuff wrapped around the patient's upper arm and brachial artery until the brachial artery occludes (i.e., no blood flow). Then, the cuff is gradually deflated and blood starts flowing with “thumping” sounds that can be detected through a stethoscope. The first “thumping” sound should occur when the cuff pressure equals the patient's systolic pressure (maximum pressure during cardiac ejection) and the last “thumping” sound should occur when the cuff pressure equals the patient's diastolic pressure (minimum pressure during cardiac filling).
  • the auscultatory method was used for clinical hypertension diagnosis and had become the standard for non-invasive blood pressure measurement.
  • the accuracy of the measured pressure value was dependent on the operator's acute detection of the heart sound and also dependent on the rate that the operator deflated the cuff.
  • an automated method was established based on detecting oscillatory pulsations measured by the brachial cuff during cuff inflation or deflation.
  • the height of the pulse oscillation increases when the cuff pressure decreases from systolic pressure to below systolic pressure and the height of the oscillation decreases when the cuff pressure decreases from above diastolic pressure to diastolic pressure and below.
  • current “oscillometric” devices apply different algorithms to detect oscillation heights related to systolic and diastolic pressure.
  • Oscillometric cuff devices are often called a non-invasive blood pressure devices or NIBP devices in the art.
  • NIBP devices To be accepted for clinical use, an NIBP device has to show equivalence to the standard auscultatory method based on the American National Standard for Non-Invasive Automated Blood Pressure Devices, see, ANSI/AAMI/ISO 81060-2:2009, “Non-invasive sphygmomanometers—Part 2: Clinical validation of automated measurement type,” Section 5.2.4.1.2 Part a—Criterion 1, page 20 (which states that the mean error for determination of all subjects in the test “shall not be greater than 5.0 mmHg with a standard deviation no greater than 8 mmHg.”) Accordingly, any oscillometric cuff device can pass the validation requirements if the average difference with the auscultatory method for systolic and diastolic pressure is not more than 5 mmHg and the standard deviation is not more than 8 mmHg. This means
  • Oscillometric automated blood pressure devices have been standard in clinical practice for many years, and have also been used in medical research to assess cardiovascular risk. Even though non-invasive blood pressure (NIBP) measurement identifies a percentage of the general population at risk of cardiovascular diseases, a large group is not identified by NIBP measurement to be at risk even though they may be at risk. The main reason is that measured blood pressure varies among different NIBP devices due to the different devices having different propriety algorithms for detecting systolic and diastolic pressure.
  • NIBP non-invasive blood pressure
  • NIBP devices have been shown to underestimate systolic pressure and overestimate diastolic pressure, see, Sharman et al, “Validation of non-invasive central blood pressure devices: Artery Society task force consensus statement on protocol standardization”, European Journal of Hypertension 2017; Cloud et al, “Estimation of central aortic pressure by SphygmoCor® requires intra-arterial peripheral”, Clinical Science (2003) 105, 219-225.Shoji et al, “Invasive validation of a novel brachial cuff-based oscillometric device (SphygmoCorXCEL) for measuring central blood pressure”, Journal of Hypertension 2016, 34. Accordingly, since measuring brachial pressure invasively is the gold standard, non-invasive measurements that closer estimate the invasive pressure and overcome the errors inherent in cuff NIBP devices would be a significant improvement in the field of blood pressure measurement and its clinical importance.
  • the general purpose of the invention is to provide a non-invasive method of measuring brachial systolic and diastolic pressure that more accurately estimates its invasive equivalent, and consequently renders brachial systolic and diastolic measurements more clinically relevant.
  • the invention applies linear and/or non-linear models to the cuff measured brachial pulse waveform based on the cardiovascular features of the arterial waveform. More specifically, the invention estimates brachial systolic and diastolic pressure values using a non-invasive cuff to measure initially non-invasive systolic and diastolic pressure and also measure a high fidelity brachial volumetric displacement waveform with it cardiovascular features preserved.
  • the invention pertains to a method of measuring a patient's brachial systolic and diastolic blood pressure non-invasively.
  • the method involves the use of a brachial cuff device having an inflatable brachial cuff, a tube, a pressure pump with a pressure control system and a pressure sensor that provides an analog signal of the pressure within the brachial cuff.
  • the cuff is wrapped around the patient's upper arm and then the brachial cuff device is used in oscillometric mode to take initial non-invasive measurements of the patient's systolic blood pressure and diastolic blood pressure.
  • the brachial cuff volumetric displacement waveform should have at least an identifiable first systolic peak, second systolic peak and incisura indicating the beginning of diastole.
  • the specific filtering necessary for preserving the waveform features is dependent on the brachial cuff type and model.
  • the recorded brachial cuff volumetric displacement waveform is initially calibrated using the patient's brachial systolic pressure and diastolic pressure as measured with the cuff in oscillometric mode.
  • the calibrated cuff waveform is then transformed into a recalibrated waveform where its maximum and minimum correspond to the patient's invasive systolic and diastolic pressure respectively.
  • this method is capable of estimating the patient's systolic and diastolic brachial blood pressure within 3 mmHg of invasively measured systolic and diastolic pressures on a consistent basis.
  • multiple recalibration equations are provided, and the selection of which recalibration equation to use is based on the detection or calculation of one or more parameters pertaining to the cardiovascular features of the initially-calibrated volumetric displacement waveform.
  • the selection of the recalibration equation can be made using a decision tree, or with other algorithms that correlate waveform features to the appropriate recalibration equations, like support vector machines, linear and non-linear regression, neural networks and so on.
  • five different recalibration equations are selected based on a decision tree. According to testing, two of the recalibration equations can apply if the augmentation index (AIx) is less than 28. In this case, one recalibration equation is used if the ejection duration (ED) is greater than or equal to 300 and another is used if the ejection duration (ED) is less than 300. If the augmentation index (AIx) is greater than or equal to 28 and the heart rate (HR) is less than 60, then a third recalibration equation is used.
  • a fourth recalibration equation is used if the ratio of the area under the curve during diastole (AUC d ) divided by the area under the curve during systole (AUC s ) is less than 100 and a fifth equation is used if that ratio is greater than or equal to 100.
  • the form of the recalibration equations in the exemplary embodiment is a combination of linear and non-linear components, where the coefficients are selected so that the output from the recalibration equations provides an estimated waveform in which the maximum value matches data for invasively-measured brachial systolic pressure and the minimum of the outputted waveform matches data for the invasively-measured brachial diastolic pressure for each of the five identified situations.
  • the inventor has discovered that a generalized linear transfer function is not capable of reliably and accurately mapping cuff measured NIBP to its invasive counterpart for the general population.
  • the inventor has also discovered that it is best to determine the recalibration equations and the selection criteria for the specific NIBP device being recalibrated, for example by comparing non-invasive data measured with the device to simultaneously collected invasive data.
  • the form of the recalibration equations includes a non-linear component, such as a sigmoid function.
  • several sets of values for equation coefficients and constants are determined independently for the various recalibration equations in accordance with specific groups of data pertaining to the decision tree selection criteria.
  • Machine learning techniques can be used to identify the decision tree criteria such that the recalibration equations for the respective groups of data result in reliably accurate recalibrated waveforms in which the maximum and minimum values are accurate estimates of invasively measured brachial systolic and diastolic pressure.
  • a non-peripheral waveform other than a brachial cuff volumetric displacement waveform can be measured, calibrated and re-calibrated for use in the invention.
  • a non-invasive sensor can be used to record data representing the patient's raw peripheral waveform, such as using a tonometer to measure and record the patient's raw radial pressure waveform.
  • the raw peripheral waveform can then be calibrated and recalibrated using a method similar to that used with the brachial cuff volumetric waveform.
  • the form of the recalibration equations will be the same or similar to those used when a brachial cuff volumetric waveform is used but the coefficients and constants are likely to be different depending on the underlying data, and the parameters for the selecting the appropriate recalibration equation is also likely to be different depending on the underlying data.
  • the invention pertains to systems capable of implementing the methods described above.
  • the system necessarily includes a brachial cuff device having a cuff, a pressure tube, a pressure control device, and a pressure sensor for outputting the raw analog signal, as well as analog or digital filters, and a digital signal processor or other computing means.
  • the NIBP-calibrated brachial cuff waveform (or other NIBP-calibrated peripheral waveform) with cardiovascular related features can be categorized based on the waveform features and expected invasive SP and DP using machine learning algorithms like support vector machine, random forest, k-nearest classification, or boosting. These algorithms will provide equations that separate the waveforms based on its features into categories where each category represents ISP and IDP range of values.
  • Another embodiment using another machine learning method like neural network such that collected data can be used to train a neural network with waveform features as inputs and the invasive SP and DP.
  • FIG. 1 illustrates the difference between non-invasive systolic and diastolic pressure (NISP/NIDP) measured by a brachial cuff-measured, and invasively measured systolic and diastolic pressure (ISP/IDP) in the brachial artery.
  • NISP/NIDP non-invasive systolic and diastolic pressure
  • ISP/IDP invasively measured systolic and diastolic pressure
  • FIG. 2 is the schematic drawing illustrating implementation of the invention, which records a non-invasive brachial cuff volumetric displacement waveform, measures NISP and NIDP using a brachial cuff device and estimates ISP and IDP in the brachial artery after recalibration of the waveform.
  • FIG. 3 shows an exemplary form of non-invasive to invasive blood pressure waveform recalibration equations for brachial pulse waveforms having different waveform shapes.
  • FIG. 4 shows and defines certain cardiovascular features of an initially calibrated (NISP/NIDP) brachial cuff volumetric displacement waveform.
  • FIG. 5 shows an example decision tree based on the initially calibrated (NISP/NIDP) brachial cuff pulse waveform features that determine which non-invasive to invasive blood pressure recalibration equation should be used.
  • FIG. 6A left plot is a plot of the average of versus the difference between NIBP and invasive brachial systolic pressure (SP).
  • Right plot is a plot of the average of versus the difference between recalibrated and invasive brachial SP.
  • the left text box shows the average, standard deviation, the maximum and the minimum difference between NIBP and invasive brachial SP.
  • the right text box shows the average, standard deviation, the maximum and the minimum difference between recalibrated and invasive brachial SP.
  • FIG. 6B left plot is a plot of the average of versus the difference between NIBP and invasive brachial diastolic pressure (DP).
  • Right plot is a plot of the average of versus the difference between recalibrated and invasive brachial DP.
  • the left text box shows the average, standard deviation, the maximum and the minimum difference between NIBP and invasive brachial DP.
  • the right text box shows the average, standard deviation, the maximum and the minimum difference between recalibrated and invasive brachial DP.
  • FIG. 7 is the schematic drawing illustrating implementation of another embodiment of the invention, which records a non-invasive radial pressure waveform with a tonometer, measures NISP and NIDP using a brachial cuff device and estimates ISP and IDP in the brachial artery after recalibration of the non-invasive radial pressure waveform.
  • FIG. 1 illustrates a brachial cuff 2 wrapped around the upper arm 102 of a patient 101 for the purpose of non-invasively measuring the patient's systolic and diastolic blood pressure in the brachial artery 103 .
  • the non-invasively measured systolic blood pressure is identified in FIG. 1 as NISP, and the non-invasively measured diastolic blood pressure is identified as NIDP.
  • NISP non-invasively measured systolic blood pressure
  • NIDP non-invasively measured diastolic blood pressure
  • FIG. 1 also illustrates measuring the patient's systolic and diastolic pressures in the brachial artery 103 invasively (e.g., using a pressure sensor with a catheter inserted into the patient's arm 102 and brachial artery 103 ).
  • the invasively measured systolic blood pressure is identified in FIG. 1 as ISP, and the invasively measured diastolic blood pressure is identified as IDP.
  • invasively measured ISP and IDP are considered to be the gold standard for clinical and research analysis and present day inflated cuff, oscillometric systems typically underestimate systolic brachial pressure (i.e., NISP ⁇ ISP) and overestimate diastolic brachial pressure (i.e., NIDP>IDP).
  • the aim of the current invention is to reduce or eliminate the difference prevalent between invasive measurements and non-invasive measurements.
  • FIG. 2 illustrates a system 100 configured in accordance with one exemplary embodiment of the invention.
  • the system 100 in FIG. 2 includes a non-invasive blood pressure unit 1 (NIBP unit 1 ), the same as or similar to a conventional brachial cuff “oscillometric” blood pressure device.
  • the NIBP unit 1 includes, e.g., a cuff 2 , a pressure tube, an air pressure control, and a pressure sensor for sensing the pressure in the cuff 2 and outputting an analog signal.
  • the NIBP unit 1 also includes control algorithms which operate in the oscillometric mode to determine NISP and NIDP, as is common in the art.
  • the NIBP unit 1 With a cuff 2 wrapped around the patient's upper arm 102 (including the brachial artery 101 ), the NIBP unit 1 performs an oscillometric brachial blood pressure measurement resulting in a value for the non-invasive brachial systolic pressure (NISP) and non-invasive brachial diastolic pressure (NIDP). Then, while the cuff 2 is inflated at a constant pressure (below NIDP, between NIDP and NISP or above NISP), the NIBP unit 1 records a raw cuff waveform 3 .
  • NISP non-invasive brachial systolic pressure
  • NIDP non-invasive brachial diastolic pressure
  • the pressure of the inflated cuff will affect the shape of the recorded waveform, and therefore it is important that the cuff be inflated with respect to NISP and NIDP consistent with the inflation of the cuff for the data collected to determine the recalibration equations discussed below. For example, if the recalibration equations are determined based on data collected with the cuff inflated below NIDP for the test population, then the raw waveform 3 should be collected with the cuff inflated below the patient's NIDP. In this embodiment, it is preferred that the inflated cuff pressure have a 10% difference or more compared the patient's DP in order to avoid borderline effects.
  • the raw cuff waveform 3 is processed through a high pass filter and low pass filter or a band pass filter 4 to produce a pre-calibrated brachial cuff waveform with cardiovascular related features 5 preserved.
  • This waveform 5 is brachial cuff volumetric displacement waveform, which contains and preserves the cardiovascular features present in the patient's brachial artery pressure waveform, however, the amplitude of the waveform 5 needs to be calibrated.
  • the filtering in an exemplary embodiment uses a low pass filter with cutoff frequency between 30 to 40 Hz , and high pass filter with pass frequency between 0.7 to 1 Hz has been found suitable to capture a raw waveform in which the cardiovascular features, including the foot, first systolic peak, second systolic peak and incisura, are preserved in the data.
  • the purpose of the low pass filter is to preserve volume, pressure or flow signal frequencies that are related to physiological function and eliminate noises related to environmental inferences such as power sources noise.
  • the choice of the low pass cutoff frequency is based on the fact that all physiological features in a pressure, volume, flow waveforms are within 25 Hz of the signal spectrum (See FIG. 26.21 in W. Nichols and M. O'Rourke, “McDonald's Blood Flow in Arteries: Theoretical, Experimental and Clinical Principles,” 5 th Edition).
  • the purpose of the high pass filter is to eliminate low frequencies related to artifacts noise as a result of arm movements, breathing effect or the tube and cuff reaction to the compliance to pressure. These low frequency artifacts, which cause signal baseline drift and can dampen signal shape, are usually below 1 Hz, hence the high pass filter pass frequency.
  • Both filters which can be implemented as a Chebyshev type filters with pass band ripple or stop band ripple of ⁇ 3 dB, can be combined into one band pass filter where it pass all frequencies between 0.7 to 40 Hz.
  • the operations after the NIBP unit 1 in FIG. 2 are preferably implemented in a digital signal processor, or other computing device.
  • the electronic filters discussed in connection with block 4 can be analog or digital, with analog-to-digital conversion occurring after block 4 or prior to block 4 , respectively.
  • Block 6 in FIG. 2 depicts both the pre-calibrated waveform 5 (with features preserved) and the NISP and NIDP values being entered into an algorithm (e.g. software code) that calibrates the pre-calibrated brachial cuff waveform 5 so that the maximum and minimum values of waveform 5 are equivalent to NISP and NIDP, respectively.
  • This initial calibration results in a NIBP-calibrated brachial cuff waveform with preserved features as indicated by reference number 10 in FIG. 2 .
  • NIMP mean pressure
  • the calibrated waveform 5 shall be considered a NIBP-calibrated waveform 5 . If this is the case, then the same calibration should occur when establishing the recalibration equations as explained in connection with FIG. 3 .”
  • the software depicted in block 6 also determines parameter values for cardiovascular related features of the NISP/NIDP calibrated brachial cuff waveform 10 . The specific cardiovascular features used in this exemplary embodiment are explained in connection with FIG. 4 .
  • the determined feature parameter values from block 6 are the input for a selection algorithm, block 7 , that determines which recalibration equation f i (x), reference number 8 , should be used to recalibrate the NIBP/NISP calibrated waveform 10 in terms of invasive brachial blood pressure instead of non-invasive brachial blood pressure.
  • a selection algorithm 7 and recalibration equations 8 are shown in FIG. 5 and FIG. 3 respectively, and are discussed in more detail below.
  • NIBP non-invasive to invasive blood pressure recalibration equations 8. More specifically, data was collected from 150 patients with wide range of brachial SP, DP (SP range from 88 to 216 mmHg and DP range from 40 to 93 mmHg) and heart rate (from 41 to 102 beats per minute) providing a representation of the general population.
  • the collected data included invasively measured brachial pressure waveform data (through fluid filled catheter with properly tested frequency response for every measurement) and contemporaneously collected NIBP measured SP and DP, and filtered NIBP brachial waveform data.
  • the cuff was inflated at 10% of the patient's NIDP to collect the filtered NIBP brachial waveforms.
  • a method of system identification was used to establish the coefficients for proposed recalibration equations 13 as shown in FIG. 3 .
  • a non-linear sigmoid function system identification method which constitutes linear and non-linear components.
  • the non-invasively collected data 12 is filtered (like block 4 ) and NIBP calibrated (like block 6 ) to represent the NIBP calibrated brachial cuff waveform and is the input for the proposed recalibration equations 13.
  • Invasively collected data 14 for the brachial artery is the output of the proposed recalibration equations 13.
  • recalibration equations 13 Given the known input 12 and output 14 from the collected data, recalibration equations 13 with unknown coefficients are proposed. Then, the coefficients are estimated such that the difference between the equation output and the data collected for the invasive blood pressure measurements is minimized.
  • the recalibration equations can theoretically be linear or non-linear or combination of both types, however, it has been found that using a non-linear component produces more accurate results.
  • f( ) is a non-linear function which in this example is a sigmoid function expressed as follow:
  • y ⁇ ( t ) ( [ u ⁇ ( t ) ⁇ ⁇ u ⁇ ( t - 1 ) ⁇ ⁇ y ⁇ ( t - 1 ) ] ⁇ [ P 1 P 2 P 3 ] ) + ( a i ⁇ f ⁇ ( ( [ u ⁇ ( t ) ⁇ ⁇ u ⁇ ( t - 1 ) ⁇ ⁇ y ⁇ ( t - 1 ) ] ⁇ [ b 1 , 1 b 2 , 1 b 3 , 1 b 2 , 1 b 2 , 2 b 2 , 3 b 3 , 1 b 3 , 2 b 3 , 3 ] ) + [ c 1 ⁇ ⁇ c 2 ⁇ ⁇ c 3 ] ) ) + d i [ 7 ]
  • the goal of the system identification method is to estimate coefficient matrices P i , B i , C i , and the constants a i , d i to minimize the difference between estimated output and the collected invasive data 14 .
  • the final form of the proposed recalibration equations 13 in FIG. 3 corresponds to the recalibration equations 8 programmed in to the system 100 , and used in practice to detect brachial SP and DP using a brachial cuff.
  • the final form of the proposed recalibration equations 13 is determined for different groupings of input 12 and output 14 waveform data, in which the groupings are based on waveform feature parameters determined by applying the system identification method.
  • the selection algorithm 7 is a decision tree, see FIG. 2 , that determines which recalibration equation 8 should be used based on waveform features.
  • FIG. 4 describes some of the brachial waveform cardiovascular related features, which are used as inputs to the selection algorithm 7 in this exemplary embodiment.
  • the cardiovascular related features and others can be detected or calculated, e.g., using the through derivative method as described in U.S. Pat. No. 5,265,011 to Michael O'Rourke, entitled “Method for ascertaining the pressure pulse and related parameters in the ascending aorta from the contour of the pressure pulse in the peripheral arteries”, which is herby incorporated by reference herein, or other suitable mathematical method in time or frequency like wavelet analysis.
  • Exemplary features that can be used by the selection algorithm include, for example, NISP, NIDP, AIx, AUCs/AUCd, P 1 , P 2 , T 1 , T 2 , and ED as described in FIG. 4 .
  • Other features like mean pressure, heart rate, cardiac period and slope of the systolic upstroke, which also can be detected from the NIBP calibrated waveform, can also be used as input to the algorithm.
  • the parameters and threshold values for the parameters in order to construct the decision tree selection algorithm 7 which selects the appropriate recalibration equation 8 to recalibrate from NISP/NIDP to ISP/IDP based on the recorded NIBP-calibrated waveform characteristics, can be determined by training decision tree algorithm to determine the threshold and structure of the tree.
  • the recalibration equations and selection algorithm, or other suitable algorithm for recalibration conversion can be developed using other types of machine learning such as support vector machine, linear and nonlinear regression, and neural network.
  • the overall purpose is to provide an algorithm in which data representing a NIBP-calibrated cuff waveform with cardiovascular features preserved serve as the input, and the maximum and minimum value of the output waveform closely estimates ISP and IDP, respectively, based on known population data.
  • FIG. 5 illustrates the operation of the selection algorithm 7 .
  • the selection algorithms 7 developed to date, based on testing and analysis, are somewhat more complicated than the algorithm shown in FIG. 5 .
  • the illustrative selection algorithm in FIG. 5 is in the form of a decision tree that is used to determine the appropriate recalibration equation 8 (NISP/NIDP to ISP/IDP) based on the detected or calculated waveform features or parameters.
  • the recalibration equations 8 are labelled Eq1, Eq2, Eq3, Eq4 and Eq5 in FIG. 5 .
  • Block 16 in FIG. 5 depicts pulse waveform features 15 being detected from the NIBP-calibrated cuff waveform 10 .
  • suitable feature detection methods include the derivative method or other mathematical methods in time or frequency domain.
  • the values detected or calculated pertaining to the waveform features 15 are the input to the decision tree 17 , which in this example serves as the selection algorithm 7 in FIG. 2 .
  • the decision tree 17 decides which recalibration equation Eq1, Eq2, Eq3, Eq4 or Eq5 to use according to the values of the detected or calculated waveform features.
  • one of five NISP/NIDP to ISP/IDP recalibration equations can be selected based on values of AIx, ED, heart rate (HR) and the percentage ratio of AUCd to AUCs.
  • the waveform parameter values for the decision tree 17 and the threshold values for the decision tree 17 are based on testing and data analysis and are disclosed for purposes of illustration. Other examples may use more waveform features with more branches in the decision tree. Also, other algorithms that correlate the waveform features with the appropriate NISP/NIDP to ISP/IDP recalibration equation like support vector machine, linear and nonlinear regression, and neural network can also be used as the selection algorithm.
  • results Using a subset of the collected data to train a decision tree where the inputs are waveform features and the outputs were the recalibration equations (Eq1, Eq2, Eq3, Eq4 and Eq5).
  • the decision tree showed, for example that if AIx is less than 28, NIDP less than 77, ED less than 330 and AIx is larger than or equal to 14 then Eq1 is chosen as the recalibration equation. If AIx is less than 28, NIDP less than 77, ED less than 330 and AIx is less than 14 then Eq2 is chosen as the recalibration equation.
  • AIx is greater than or equal to 28, NIDP greater than or equal to 85, the percentage ratio of AUCd to AUCs is greater than or equal to 100, HR less than 60 and ED is greater than or equal to 300 then Eq3 is chosen as the recalibration equation. If AIx is greater than or equal to 28, NIDP greater than or equal to 85, HR larger than or equal 60, and the percentage ratio of AUCd to AUCs is less than 100, then Eq4 is chosen as the recalibration equation. If AIx is greater than or equal to 28, NIDP greater than or equal to 85, HR larger than or equal 60, and the percentage ratio of AUCd to AUCs is greater than or equal to 100, then Eq5 is chosen as the recalibration equation.
  • the graph on the left in FIG. 6A shows the plot of the average of versus the difference between NIBP and invasive brachial systolic pressure (SP).
  • SP invasive brachial systolic pressure
  • the graph on the right in FIG. 6A shows large, significant reductions in the difference between the recalibrated and the invasive brachial SP—illustrating the accuracy of the recalibration.
  • the average and standard deviation of the difference were reduced significantly from ⁇ 11 ⁇ 15 mmHg to 0 ⁇ 4 mmHg.
  • the graph on the left in FIG. 6B shows the plot of the average of versus the difference between NIBP and invasive brachial diastolic pressure (DP).
  • the graph on the right in FIG. 6B shows large, significant reductions in the difference between the recalibrated and the invasive brachial DP illustrating the accuracy of the recalibration.
  • the average and standard deviation of the difference were reduced significantly from 10 ⁇ 6 mmHg to 0 ⁇ 3 mmHg.
  • the NIBP-calibrated brachial cuff waveform with cardiovascular related features can be categorized based on the waveform features and expected invasive SP and DP using machine learning algorithms like support vector machine, random forest, k-nearest classification, or boosting. These algorithms will provide equations that separate the waveforms based on its features into categories where each category represents ISP and IDP range of values.
  • Another embodiment using another machine learning method like neural network such that collected data can be used to train a neural network with waveform features as inputs and the invasive SP and DP. The advantage of these embodiments that they do not require specific recalibration equations and use a single general method to estimate invasive SP and DP from the NIBP-calibrated brachial cuff waveform with cardiovascular related features.
  • FIG. 7 illustrates a system 200 configured in accordance with another exemplary embodiment of the invention.
  • This system 200 is similar to system 100 shown in FIG. 2 except it uses a tonometer 202 to measure a raw radial pressure waveform 203 , rather than the cuff to measure a raw cuff waveform. Similar reference numbers are used in FIG. 7 as in FIG. 2 to represent similar components.
  • the system 200 in FIG. 7 includes a non-invasive blood pressure unit 1 (NIBP unit 1 ), which is the same as or similar to a conventional brachial cuff “oscillometric” blood pressure device.
  • NIBP unit 1 non-invasive blood pressure unit 1
  • the NIBP unit 1 includes, e.g., a cuff 2 , a pressure tube, an air pressure control, and a pressure sensor for sensing the pressure in the cuff 2 .
  • the NIBP unit 1 also includes control algorithms which operate in the oscillometric mode to determine NISP and NIDP, as is common in the art. With a cuff 2 wrapped around the patient's upper arm (including the brachial artery), the NIBP unit 1 performs an oscillometric brachial blood pressure measurement resulting in a value for the non-invasive brachial systolic pressure (NISP) and non-invasive brachial diastolic pressure (NIDP). Then, the tonometer 202 is used to capture the raw waveform 203 .
  • NISP non-invasive brachial systolic pressure
  • NIDP non-invasive brachial diastolic pressure
  • the raw peripheral waveform 203 is processed through a high pass filter and low pass filter or a band pass filter 204 to remove low and high frequency noise and produce a pre-calibrated peripheral waveform with cardiovascular related features 205 preserved.
  • This waveform 205 contains and preserves the cardiovascular features present in the patient's peripheral pressure waveform, however, the amplitude of the waveform 205 needs to be calibrated.
  • the operations after the NIBP unit 1 in FIG. 7 are preferably implemented in a digital signal processor, or other computing device.
  • the electronic filters discussed in connection with block 204 can be analog or digital, with analog-to-digital conversion occurring after block 204 or prior to block 204 , respectively.
  • Block 206 in FIG. 7 depicts both the pre-calibrated waveform 205 (with features preserved) and the NISP and NIDP values being entered into an algorithm (e.g. software code) that calibrates the pre-calibrated peripheral pressure waveform 205 so that the maximum and minimum values of waveform 205 are equivalent to NISP and NIDP, respectively.
  • This initial calibration results in a NIBP-calibrated peripheral pressure waveform with preserved features as indicated by reference number 210 in FIG. 7 .
  • NIMP mean pressure
  • the calibrated waveform 205 shall be considered a MBP-calibrated waveform 205 . If this is the case, then the same calibration should occur when establishing the recalibration equations as explained in connection with FIG. 3 .
  • the software depicted in block 206 also determines parameter values for cardiovascular related features of the NISP/NIDP calibrated peripheral pressure waveform 210 . Cardiovascular features used in this exemplary embodiment are the same as explained in connection with FIG. 4 .
  • the determined feature parameter values from block 206 are the input for a selection algorithm, block 207 , that determines which recalibration equation f i (x), reference number 208 , should be used to recalibrate the NIBP/NISP calibrated waveform 210 in terms of invasive brachial blood pressure instead of non-invasive brachial blood pressure.
  • the selection algorithm 207 and recalibration equations 208 may take the form described in FIGS. 3 and 5 , although the coefficients and constant values for the recalibration equations and the selection criteria need to be fitted to data for the peripheral pressure waveform, instead of the cuff waveform.

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